Data Science in Action
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The document discusses various applications of data science across multiple industries, including pharmaceutical, financial services, project management, human resources, and marketing. It highlights the role of advancements in technology such as AI and machine learning in optimizing processes like drug discovery, transaction analysis, and recruitment. The speaker shares their journey, emphasizing the importance of data quality, feature selection, and effective communication of results to stakeholders.
Data Science in Action
- 1. Data Science in Action Applications of Data Science in Drug Discovery, Financial Services, Project Management, Human Resources and Marketing
- 3. What this talk is about My Journey - Change of career - Lessons learned Industries covered - Pharmaceutical - Project management - Financial services within a major UK bank - Human resources and recruitment - Marketing
- 4. Druuuuugssssss *the boring kind Pubmed: a repository of all medical/biological related literature Every day hundreds of papers are published. How can an expert in ontology know about advances in diabetes research that will impact his/her research? Expediate the arrival at a Eureka! moment Benevolent AI Headquartered in London UK Startup Partnered with Astrazeneca
- 5. AI to the rescue! - GPUs - TPUs - Lots of money Combining free unstructured text with hand curated databases in chemical-drug protein-gene gene-drug databases to achieve insight and aid the drug discovery scientist reach their Eureka! moment. NLP - POS tagging - Syntactic parsing - Entity detection Graph Theory - Inferred edges - Path analysis Reinforcement Learning - Software engineers - Data Scientists - Bionformaticians - Drug discovery scientists - Clever business type people
- 6. Show me the money! Onset of open banking in the UK Major banks want to get ahead of the curve by extracting maximal insight from their data Millins of transactional data Transaction Classification -- what is a salary payment? Are we losing money to competitors when it comes to savings? Pensions: how to identify trends in pensions and attract/retain customers Mudano Consultancy Startup Visionaries in implementing ai in the project management domain.
- 7. Give me time! Give me clarity! Think Kanban, Atlassian JIRA Think like a project manager-- how do I keep track of all tickets of all projects of all my human resources? How do I streamline a project to cut waste maximise production? How do I identify pain points in a project? I.e. issues that might delay delivery?
- 8. I want to hire the best! Think like a recruiter. How do I identify the optimum person for a job? - Specific experience - Specific qualification - Likely to be ready to move - Go beyond keyword matches Often the client knows the person they want to hire -- and want someone similar! Pre-seed startup Emerged from a startup incubator in London (Founders Factory)
- 9. What to look for? - Compare the companies a candidate worked for - Large companies are distinctly different to smaller ones - Similarity matrix -- dimensionality reduction - Look for candidates who have similar job titles - Semantic search - From job descriptions - Propensity to move: who is likely to be open to new job opportunities?
- 10. Who should I advertise to? - Identifying target audiences online - Demographics - Online behaviour - Location data Want to know: - Who is likely to visit a store - Who is likely to click on a link - Keeping things inline with GDPR Part of the Ominicom Group Operates like a startup (flat management, fluid job description room to innovate) With the backing of a large organisation
- 11. The common theme? Recommender Systems Recommend -- - The protein which activates a gene - A drug that activates the protein - The ideal candidate for a job - The most relevant notifications for a project - The right audience for an advertisement
- 13. Getting the data-- - Buy it - Scrape it - Mine it (via apps cookies etc.) - Download it
- 14. Identifying data quality: startup pitfall - Many startups jump straight into the ds model - Don't allow time for data quality checks - Or understanding the data - Decide on the list of desirables in advance - Check for missing variables - Correlations - Check the volume of data when joined to other data sets - Ask: can we impute the data? - What can we do with missing/incomplete/inaccurate data?
- 15. The Data Science Model: circle of life - Don't be clever - Start simple - Iterate - Play with the data! Feature selection, feature engineering - Understand the data -- gain a little domain knowledge - Measured by whether you can hold a conversation with a domain expert - Understand what is required! - Determine what the desired outcome is - Good precision or recall - Auc - If unsupervised how to determine quality - Specific gain for the business? (more revenue? More efficient work? New discoveries) - Precision might come at the expense of discovery! - Recall might be at the expense of efficiency
- 16. Supervised vs unsupervised - Do you have training data? - Is that training data reliable? - What is the source? - Mechanical Turk? - Expert annotation? - Is it biased? - Is my training data copious? - Can I combine golden corpora with silver?
- 17. ML vs DL - Do you need transparency or explainability? - E.g. legal or financial services - How much data do you have? - Does it support DL? - Do you have the technology? - Time? - Money? - DL models on GPUs are expensive
- 18. Presenting results Conveying the: - Significance - Importance - Limitations Of a project to stakeholders/clients/management - People who are non-experts in the field
- 19. Presenting the results: pearls of wisdom - Don’t lie - Don’t exaggerate - Be clear - Be honest - Try to think like a stakeholder - What do they want from the project? - How do I present the importance and usefulness of the results? - Explain the benefit of the complicates/time consuming/ expensive DS approach with the easier/cheaper faster methods